Mixed-SCORE+ for mixed membership community detection
Huan Qing, Jingli Wang

TL;DR
Mixed-SCORE+ is a novel method for mixed membership community detection that combines advantages of previous SCORE-based approaches, effectively handling weak signal networks and demonstrating superior performance on real-world datasets.
Contribution
It introduces Mixed-SCORE+ which integrates K+1 eigenvectors and vertex hunting steps, improving detection accuracy over existing methods in weak signal networks.
Findings
Significant error rate reduction on benchmark networks
Effective detection in weak signal networks
Strong performance on SNAP ego-networks
Abstract
Mixed-SCORE is a recent approach for mixed membership community detection proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks. In this paper, we propose a method called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the proposed method, we consider K+1 eigenvectors when there are K communities to detect weak signal networks. And we also construct vertices hunting and membership reconstruction steps to solve the problem of mixed membership community detection. Compared with several benchmark methods, numerical results show that Mixed-SCORE+ provides a significant improvement on the Polblogs network and two weak signal networks Simmons and Caltech, with…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Advanced Clustering Algorithms Research
